SMHS SciVisualization

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Scientific Methods for Health Sciences - Scientific Visualization

Questions

  • How and why should we “look” at data?
  • What data characteristics are important for exploratory data analytics (EDAs)?
  • Scientific Data-driven or Simulation-driven visualization methods may be classified in many alternative ways. Visualization techniques can be classified according to many criteria:

    SMHS SciVisualization1.png

    • Data Type: structured/unstructured, small/large, complete/incomplete, time/space, ascii/binary, Euclidean/non-Euclidean, etc.

    • Task type: Task type is one of the aspects considered in classification of visualization techniques, which provides means of interaction between the researcher, the data and the display software/platform

    • Scalability: Visualization techniques are subject to some limitations, such as the amount of data that a particular technique can exhibit

    • Dimensionality: Visualization techniques can also be classified according to the number of attributes

    • Positioning and Attributes: the distribution of attributes on the chart may affect the interpretation of the display representation, e.g., correlation analysis, where the relative distance among the plotted attributes is relevant for observation

    • Investigative Need: the specific scientific question or exploratory interest may also determine the type of visualization:

    o Examining the composition of the data

    o Exploring the distribution of the data

    o Contrasting or comparing several data elements, relations, association

    o Unsupervised exploratory data mining

    http://www.socr.umich.edu/CSCD/html/Cores/Macore2/SciViz.html

    SOCR, Excel and R Charts

    Complex Network Visualization




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